摘要
煤粉锅炉炉膛火焰温度场的测量是燃烧调整的基础,对于锅炉燃烧经济性、安全性诊断以及优化运行有着重要的意义。文中利用4个面阵CCD镜头来获取火焰图像的数字信号,在分析测试系统物理模型和炉膛火焰温度分布规律的基础上,建立了非线性优化数学模型,并对其利用基于向量评估的改进微粒群算法进行了求解。为验证测试模型的正确性,进行了数值模拟,并且,在某电站350MW锅炉上进行了多负荷工况下的实际测试。结果表明,重建的温度场可以作为燃烧诊断和优化运行的重要依据。
Flame temperature field distribution measurement is very important for the regulation, diagnosis and optimization of a coal power fired boiler. With 4 charge-coupled device （CCD） cameras, digital data of a flame image can be obtained, base on optic geometric and heat transfer theory, a section temperature field reconstruction technique combined with improved vector evaluated particle swarm optimization （VEPSO） algorithm was developed. To validate this model, numeric simulations were carried out and a series of experiments were performed in a 350MW power plant. The results indicate the model＇s reliability and prospective application in the diagnosis and optimization of a boiler.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2007年第14期13-17,共5页
Proceedings of the CSEE
关键词
微粒群算法
多目标优化
截面温度场
电荷电偶器件
火焰图像
particle swarm optimization algorithm
multi-object optimization
section temperature field
chargecoupled device
flame image
